This repository contains an exploratory data analysis (EDA) of a comprehensive dataset on ultra-marathon running events. The dataset spans over two centuries and provides detailed information on various ultra-marathon races, athletes, and their performances.

The dataset used in this analysis is sourced from Kaggle: The Big Dataset of Ultra-Marathon Running. It includes information on race events, distances, athlete demographics, and performance metrics.

The Jupyter notebook EDA_marathon_running.ipynb covers the following steps:

1. Data Acquisition

   • Downloading the dataset using the Kaggle API.
   • Extracting the data from the downloaded zip file.

2. Data Preprocessing

   • Loading the dataset into a pandas DataFrame.
   • Initial exploration of the dataset (shape, data types, missing values).
   • Filtering the dataset for specific criteria (e.g., events in 2020, races held in the USA).
   • Cleaning and transforming data columns for analysis.

3. Exploratory Data Analysis (EDA)

   • Visualizing the distribution of race distances and athlete genders.
   • Analyzing the relationship between athlete age and performance.
   • Comparing average speeds across different event distances and genders.

Several visualizations are generated to understand the data better:

• Count plots for event distances and athlete genders.
• Violin plots for athlete speeds across event distances.
• Line plots showing the relationship between athlete age and average speed.

To run the notebook, you need the following Python libraries:

• pandas
• seaborn
• matplotlib
• kaggle

This analysis provides insights into ultra-marathon running events, highlighting trends and patterns in athlete performances based on gender, age, and event distances. Further analysis can be conducted to explore additional aspects of the dataset.